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15 Data Analysis Examples

data analysis examples and definition, explained below

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, derive conclusions, and support decision-making (Upton & Brawn, 2023).

It encompasses a variety of techniques from statistics, mathematics, and computer science to interpret complex data structures and extract meaningful insights (Bekes & Kezdi, 2021).

We use data analysis to generate useful insights from data that can help in our decision-making and strategic planning in various realms. For example:

  • It can help businesses to develop a better understanding of market trends and customer preferences to inform marketing strategies.
  • We can develop a modeled understanding of risks and prevent issues before they escalate into larger problems.
  • Data analysis may reveal hidden or not easily identifiable insights and trends, empowering you to enrich your knowledge base and anticipate future needs (Naeem et al., 2020; Upton & Brawn, 2023).

Below are some common ways that data analysis is conducted.

Data Analysis Examples

1. Sales Trend Analysis This type of data analysis involves assessing sales data over various periods to identify trends and patterns. For instance, a retail company might monitor its quarterly sales data to identify peak buying times or popular products (Bihani & Patil, 2014). Such analysis allows businesses to adjust their sales strategies, inventory management, and marketing efforts to align with customer demands and seasonal trends, thereby enhancing profitability and operational efficiency (Kohavi, Rothleder & Simoudis, 2002).

2. Customer Segmentation In this data analysis example, businesses compartmentalize their customer base into different groups based on specific criteria such as purchasing behavior, demographics, or preferences (Kohavi, Rothleder & Simoudis, 2002). For example, an online shopping platform might segment its customers into categories like frequent buyers, seasonal shoppers, or budget buyers. This analysis helps tailor marketing campaigns and product offerings to appeal to each group specifically, enabling improved customer engagement and business growth.

3. Social Media Sentiment Analysis This is a popular use of data analysis in the digital age. Companies harness big data from social media platforms to analyze public sentiment towards their products or brand. By examining comments, likes, shares, and other interactions, they can gauge overall satisfaction and identify areas for improvement. This kind of scrutiny can significantly impact a business’s online reputation management and influence its marketing and public relations strategies.

4. Forecasting and Predictive Analysis Businesses often use data analysis to predict future trends or outcomes. For instance, an airline company might analyze past data on seat bookings, flight timings, and passenger preferences to forecast future travel trends. This predictive analysis allows the airline to optimize its flight schedules, plan for peak travel periods, and set competitive ticket prices, ultimately contributing to improved customer satisfaction and increased revenues.

5. Operational Efficiency Analysis This form of data analysis is focused on optimizing internal processes within an organization. For example, a manufacturing company might analyze data regarding machine performance, maintenance schedules, and production output to identify bottlenecks or inefficiencies (Bihani & Patil, 2014). By addressing these issues, the company can streamline its operations, improve productivity, and reduce costs, signifying the importance of data analysis in achieving operational excellence.

6. Risk Assessment Analysis This type of data analysis helps businesses identify potential risks that could adversely impact their operations or profits. An insurance company, for instance, might analyze customer data and historical claim information to estimate future claim risks. This supports more accurate premium setting and helps in proactively managing any potential financial hazards, underscoring the role of data analysis in sound risk management.

7. Recruitment and Talent Management Analysis In this example of data analysis, human resources departments scrutinize data concerning employee performance, retention rates, and skill sets. For example, a technology firm might conduct analysis to identify the skills and experience most prevalent among its top-performing employees (Chang, Wang & Hawamdeh, 2019). This enables the company to attract and retain high-caliber talent, tailor training programs, and improve overall workforce effectiveness.

8. Supply Chain Optimization Analysis This form of data analysis aims to enhance the efficiency of a business’s supply chain. For instance, a grocery store might examine sales data, warehouse inventory levels, and supplier delivery times to ensure the right products are in stock at the right time (Chang, Wang & Hawamdeh, 2019). This can reduce warehousing costs, minimize stockouts or overstocks, and increase customer satisfaction, marking data analysis’s role in streamlining supply chains.

9. Web Analytics In this digital age, businesses invest in data analysis to optimize their online presence and functionality. An ecommerce business, for example, might analyze website traffic data, bounce rates, conversion rates, and user engagement metrics. This analysis can guide website redesign, enhance user experience, and boost conversion rates, reflecting the importance of data analysis in digital marketing and web optimization.

10. Medical and Healthcare Analysis Data analysis plays a crucial role in the healthcare sector. A hospital might analyze patient data, disease patterns, treatment outcomes, and so forth. This can support evidence-based treatment plans, inform research on healthcare trends, and contribute to policy development (Islam et al., 2018). It can also enhance patient care by identifying efficient treatment paths and reducing hospitalization time, underlining the significance of data analysis in the medical field.

11. Fraud Detection Analysis In the financial and banking sector, data analysis plays a paramount role in identifying and mitigating fraudulent activities. Banks might analyze transaction data, account activity, and user behavior trends to detect abnormal patterns indicative of fraud. By alerting the concerned authorities about the suspicious activity, such analysis can prevent financial losses and protect customer assets, illustrating data analysis’s importance in ensuring financial security.

12. Energy Consumption Analysis Utilities and energy companies often use data analysis to optimize their energy distribution and consumption. By evaluating data on customer usage patterns, peak demand times, and grid performance, companies can enhance energy efficiency, optimize their grid operations, and develop more customer-centric services. It shows how data analysis can contribute to a more sustainable and efficient use of resources.

13. Market Research Analysis Many businesses rely on data analysis to gauge market dynamics and consumer behaviors. A cosmetic brand, for example, might analyze sales data, consumer feedback, and competitor information. Such analysis can provide useful insights about consumer preferences, popular trends, and competitive strategies, facilitating the development of products that align with market demands, showcasing how data analysis can drive business innovation.

14. Quality Control Analysis Manufacturing industries often use data analysis in their quality control processes. They may monitor operational data, machine performance, and product fault reports. By identifying causes of defects or inefficiencies, these industries can improve product quality, enhance manufacturing processes, and reduce waste, demonstrating the decisive role of data analysis in maintaining high-quality standards.

15. Economic and Policy Analysis Government agencies and think tanks utilize data analysis to inform policy decisions and societal strategies. They might analyze data relating to employment rates, GDP, public health, or educational attainment. These insights can inform policy development, assess the impact of existing policies, and guide strategies for societal improvement. This reveals that data analysis is a key tool in managing social and economic progression.

For more General Examples of Analysis, See Here

Data analysis, encompassing activities such as trend spotting, risk assessment, predictive modeling, customer segmentation, and much more, proves to be an indispensable tool in various fields.

From optimizing operations and making informed decisions to understanding customer behavior and predicting future trends, its applications are diverse and far-reaching. Through meticulous examination of relevant data and astute interpretation of patterns, businesses and organizations can extract actionable insights, enhance their strategic planning, and bolster their competitive advantage.

Furthermore, with the current growth in digital technology, the potency of data analysis in enhancing operational efficiency, facilitating innovation, and driving economic growth cannot be overstated. Therefore, mastery of data analysis techniques and methodologies is critical for anyone seeking to harness the full potential of their data.

Ultimately, data analysis seeks to turn raw data into valuable knowledge, enabling organizations and individuals to thrive in today’s data-driven world.

Bekes, G., & Kezdi, G. (2021). Data Analysis for Business, Economics, and Policy . Cambridge University Press.

Bihani, P., & Patil, S. T. (2014). A comparative study of data analysis techniques.  International journal of emerging trends & technology in computer science ,  3 (2), 95-101.

Chang, H. C., Wang, C. Y., & Hawamdeh, S. (2019). Emerging trends in data analytics and knowledge management job market: extending KSA framework.  Journal of Knowledge Management ,  23 (4), 664-686. doi: https://doi.org/10.1108/JKM-02-2018-0088

Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018, May). A systematic review on healthcare analytics: application and theoretical perspective of data mining. In  Healthcare  (Vol. 6, No. 2, p. 54). doi: https://doi.org/10.3390/healthcare6020054

Kohavi, R., Rothleder, N. J., & Simoudis, E. (2002). Emerging trends in business analytics .  Communications of the ACM ,  45 (8), 45-48.

Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., … & De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data. In  Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15–18 October 2019, Arad, Romania  (pp. 309-325). Springer Singapore.

Upton, G., & Brawn, D. (2023). Data Analysis: A Gentle Introduction for Future Data Scientists . Oxford: Oxford University Press.

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Home Market Research

Data Analysis in Research: Types & Methods

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Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

  • Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
  • Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
  • Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

  • Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
  • Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
  • Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
  • Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

  • Fraud: To ensure an actual human being records each response to the survey or the questionnaire
  • Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
  • Procedure: To ensure ethical standards were maintained while collecting the data sample
  • Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

  • Count, Percent, Frequency
  • It is used to denote home often a particular event occurs.
  • Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

  • Mean, Median, Mode
  • The method is widely used to demonstrate distribution by various points.
  • Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

  • Range, Variance, Standard deviation
  • Here the field equals high/low points.
  • Variance standard deviation = difference between the observed score and mean
  • It is used to identify the spread of scores by stating intervals.
  • Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

  • Percentile ranks, Quartile ranks
  • It relies on standardized scores helping researchers to identify the relationship between different scores.
  • It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

  • Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
  • Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

  • Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
  • Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
  • Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
  • Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
  • Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

  • The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
  • Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
  • The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Data analysis write-ups

What should a data-analysis write-up look like.

Writing up the results of a data analysis is not a skill that anyone is born with. It requires practice and, at least in the beginning, a bit of guidance.

Organization

When writing your report, organization will set you free. A good outline is: 1) overview of the problem, 2) your data and modeling approach, 3) the results of your data analysis (plots, numbers, etc), and 4) your substantive conclusions.

1) Overview Describe the problem. What substantive question are you trying to address? This needn’t be long, but it should be clear.

2) Data and model What data did you use to address the question, and how did you do it? When describing your approach, be specific. For example:

  • Don’t say, “I ran a regression” when you instead can say, “I fit a linear regression model to predict price that included a house’s size and neighborhood as predictors.”
  • Justify important features of your modeling approach. For example: “Neighborhood was included as a categorical predictor in the model because Figure 2 indicated clear differences in price across the neighborhoods.”

Sometimes your Data and Model section will contain plots or tables, and sometimes it won’t. If you feel that a plot helps the reader understand the problem or data set itself—as opposed to your results—then go ahead and include it. A great example here is Tables 1 and 2 in the main paper on the PREDIMED study . These tables help the reader understand some important properties of the data and approach, but not the results of the study itself.

3) Results In your results section, include any figures and tables necessary to make your case. Label them (Figure 1, 2, etc), give them informative captions, and refer to them in the text by their numbered labels where you discuss them. Typical things to include here may include: pictures of the data; pictures and tables that show the fitted model; tables of model coefficients and summaries.

4) Conclusion What did you learn from the analysis? What is the answer, if any, to the question you set out to address?

General advice

Make the sections as short or long as they need to be. For example, a conclusions section is often pretty short, while a results section is usually a bit longer.

It’s OK to use the first person to avoid awkward or bizarre sentence constructions, but try to do so sparingly.

Do not include computer code unless explicitly called for. Note: model outputs do not count as computer code. Outputs should be used as evidence in your results section (ideally formatted in a nice way). By code, I mean the sequence of commands you used to process the data and produce the outputs.

When in doubt, use shorter words and sentences.

A very common way for reports to go wrong is when the writer simply narrates the thought process he or she followed: :First I did this, but it didn’t work. Then I did something else, and I found A, B, and C. I wasn’t really sure what to make of B, but C was interesting, so I followed up with D and E. Then having done this…” Do not do this. The desire for specificity is admirable, but the overall effect is one of amateurism. Follow the recommended outline above.

Here’s a good example of a write-up for an analysis of a few relatively simple problems. Because the problems are so straightforward, there’s not much of a need for an outline of the kind described above. Nonetheless, the spirit of these guidelines is clearly in evidence. Notice the clear exposition, the labeled figures and tables that are referred to in the text, and the careful integration of visual and numerical evidence into the overall argument. This is one worth emulating.

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What is data analysis? Examples and how to get started

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Even with years of professional experience working with data, the term "data analysis" still sets off a panic button in my soul. And yes, when it comes to serious data analysis for your business, you'll eventually want data scientists on your side. But if you're just getting started, no panic attacks are required.

Table of contents:

Quick review: What is data analysis?

Why is data analysis important, types of data analysis (with examples), data analysis process: how to get started, frequently asked questions.

Zapier is the leader in workflow automation—integrating with 6,000+ apps from partners like Google, Salesforce, and Microsoft. Use interfaces, data tables, and logic to build secure, automated systems for your business-critical workflows across your organization's technology stack. Learn more .

Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals. 

Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative data (e.g., surveys and case studies) to paint the whole picture. Here are two simple examples (of a nuanced topic) to show you what I mean.

An example of quantitative data analysis is an online jewelry store owner using inventory data to forecast and improve reordering accuracy. The owner looks at their sales from the past six months and sees that, on average, they sold 210 gold pieces and 105 silver pieces per month, but they only had 100 gold pieces and 100 silver pieces in stock. By collecting and analyzing inventory data on these SKUs, they're forecasting to improve reordering accuracy. The next time they order inventory, they order twice as many gold pieces as silver to meet customer demand.

An example of qualitative data analysis is a fitness studio owner collecting customer feedback to improve class offerings. The studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.

Here's why it's worth implementing data analysis for your business:

Understand your target audience: You might think you know how to best target your audience, but are your assumptions backed by data? Data analysis can help answer questions like, "What demographics define my target audience?" or "What is my audience motivated by?"

Inform decisions: You don't need to toss and turn over a decision when the data points clearly to the answer. For instance, a restaurant could analyze which dishes on the menu are selling the most, helping them decide which ones to keep and which ones to change.

Adjust budgets: Similarly, data analysis can highlight areas in your business that are performing well and are worth investing more in, as well as areas that aren't generating enough revenue and should be cut. For example, a B2B software company might discover their product for enterprises is thriving while their small business solution lags behind. This discovery could prompt them to allocate more budget toward the enterprise product, resulting in better resource utilization.

Identify and solve problems: Let's say a cell phone manufacturer notices data showing a lot of customers returning a certain model. When they investigate, they find that model also happens to have the highest number of crashes. Once they identify and solve the technical issue, they can reduce the number of returns.

There are five main types of data analysis—with increasingly scary-sounding names. Each one serves a different purpose, so take a look to see which makes the most sense for your situation. It's ok if you can't pronounce the one you choose. 

Types of data analysis including text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.

Text analysis: What is happening?

Text analysis, AKA data mining , involves pulling insights from large amounts of unstructured, text-based data sources : emails, social media, support tickets, reviews, and so on. You would use text analysis when the volume of data is too large to sift through manually. 

Here are a few methods used to perform text analysis, to give you a sense of how it's different from a human reading through the text: 

Word frequency identifies the most frequently used words. For example, a restaurant monitors social media mentions and measures the frequency of positive and negative keywords like "delicious" or "expensive" to determine how customers feel about their experience. 

Language detection indicates the language of text. For example, a global software company may use language detection on support tickets to connect customers with the appropriate agent. 

Keyword extraction automatically identifies the most used terms. For example, instead of sifting through thousands of reviews, a popular brand uses a keyword extractor to summarize the words or phrases that are most relevant. 

Because text analysis is based on words, not numbers, it's a bit more subjective. Words can have multiple meanings, of course, and Gen Z makes things even tougher with constant coinage. Natural language processing (NLP) software will help you get the most accurate text analysis, but it's rarely as objective as numerical analysis. 

Statistical analysis: What happened?

Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.

Descriptive analysis

Descriptive analysis looks at numerical data and calculations to determine what happened in a business. Companies use descriptive analysis to determine customer satisfaction , track campaigns, generate reports, and evaluate performance. 

Here are a few methods used to perform descriptive analysis: 

Measures of frequency identify how frequently an event occurs. For example, a popular coffee chain sends out a survey asking customers what their favorite holiday drink is and uses measures of frequency to determine how often a particular drink is selected. 

Measures of central tendency use mean, median, and mode to identify results. For example, a dating app company might use measures of central tendency to determine the average age of its users.

Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field. 

Inferential analysis

Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large. 

Here are a few methods used when performing inferential analysis: 

Hypothesis testing identifies which variables impact a particular topic. For example, a business uses hypothesis testing to determine if increased sales were the result of a specific marketing campaign. 

Confidence intervals indicates how accurate an estimate is. For example, a company using market research to survey customers about a new product may want to determine how confident they are that the individuals surveyed make up their target market. 

Regression analysis shows the effect of independent variables on a dependent variable. For example, a rental car company may use regression analysis to determine the relationship between wait times and number of bad reviews. 

Diagnostic analysis: Why did it happen?

Diagnostic analysis, also referred to as root cause analysis, uncovers the causes of certain events or results. 

Here are a few methods used to perform diagnostic analysis: 

Time-series analysis analyzes data collected over a period of time. A retail store may use time-series analysis to determine that sales increase between October and December every year. 

Data drilling uses business intelligence (BI) to show a more detailed view of data. For example, a business owner could use data drilling to see a detailed view of sales by state to determine if certain regions are driving increased sales.

Correlation analysis determines the strength of the relationship between variables. For example, a local ice cream shop may determine that as the temperature in the area rises, so do ice cream sales. 

Predictive analysis: What is likely to happen?

Predictive analysis aims to anticipate future developments and events. By analyzing past data, companies can predict future scenarios and make strategic decisions.  

Here are a few methods used to perform predictive analysis: 

Machine learning uses AI and algorithms to predict outcomes. For example, search engines employ machine learning to recommend products to online shoppers that they are likely to buy based on their browsing history. 

Decision trees map out possible courses of action and outcomes. For example, a business may use a decision tree when deciding whether to downsize or expand. 

Prescriptive analysis: What action should we take?

The highest level of analysis, prescriptive analysis, aims to find the best action plan. Typically, AI tools model different outcomes to predict the best approach. While these tools serve to provide insight, they don't replace human consideration, so always use your human brain before going with the conclusion of your prescriptive analysis. Otherwise, your GPS might drive you into a lake.

Here are a few methods used to perform prescriptive analysis: 

Lead scoring is used in sales departments to assign values to leads based on their perceived interest. For example, a sales team uses lead scoring to rank leads on a scale of 1-100 depending on the actions they take (e.g., opening an email or downloading an eBook). They then prioritize the leads that are most likely to convert. 

Algorithms are used in technology to perform specific tasks. For example, banks use prescriptive algorithms to monitor customers' spending and recommend that they deactivate their credit card if fraud is suspected. 

The actual analysis is just one step in a much bigger process of using data to move your business forward. Here's a quick look at all the steps you need to take to make sure you're making informed decisions. 

Circle chart with data decision, data collection, data cleaning, data analysis, data interpretation, and data visualization.

Data decision

As with almost any project, the first step is to determine what problem you're trying to solve through data analysis. 

Make sure you get specific here. For example, a food delivery service may want to understand why customers are canceling their subscriptions. But to enable the most effective data analysis, they should pose a more targeted question, such as "How can we reduce customer churn without raising costs?" 

These questions will help you determine your KPIs and what type(s) of data analysis you'll conduct , so spend time honing the question—otherwise your analysis won't provide the actionable insights you want.

Data collection

Next, collect the required data from both internal and external sources. 

Internal data comes from within your business (think CRM software, internal reports, and archives), and helps you understand your business and processes.

External data originates from outside of the company (surveys, questionnaires, public data) and helps you understand your industry and your customers. 

You'll rely heavily on software for this part of the process. Your analytics or business dashboard tool, along with reports from any other internal tools like CRMs , will give you the internal data. For external data, you'll use survey apps and other data collection tools to get the information you need.

Data cleaning

Data can be seriously misleading if it's not clean. So before you analyze, make sure you review the data you collected.  Depending on the type of data you have, cleanup will look different, but it might include: 

Removing unnecessary information 

Addressing structural errors like misspellings

Deleting duplicates

Trimming whitespace

Human checking for accuracy 

You can use your spreadsheet's cleanup suggestions to quickly and effectively clean data, but a human review is always important.

Data analysis

Now that you've compiled and cleaned the data, use one or more of the above types of data analysis to find relationships, patterns, and trends. 

Data analysis tools can speed up the data analysis process and remove the risk of inevitable human error. Here are some examples.

Spreadsheets sort, filter, analyze, and visualize data. 

Business intelligence platforms model data and create dashboards. 

Structured query language (SQL) tools manage and extract data in relational databases. 

Data interpretation

After you analyze the data, you'll need to go back to the original question you posed and draw conclusions from your findings. Here are some common pitfalls to avoid:

Correlation vs. causation: Just because two variables are associated doesn't mean they're necessarily related or dependent on one another. 

Confirmation bias: This occurs when you interpret data in a way that confirms your own preconceived notions. To avoid this, have multiple people interpret the data. 

Small sample size: If your sample size is too small or doesn't represent the demographics of your customers, you may get misleading results. If you run into this, consider widening your sample size to give you a more accurate representation. 

Data visualization

Last but not least, visualizing the data in the form of graphs, maps, reports, charts, and dashboards can help you explain your findings to decision-makers and stakeholders. While it's not absolutely necessary, it will help tell the story of your data in a way that everyone in the business can understand and make decisions based on. 

Automate your data collection

Data doesn't live in one place. To make sure data is where it needs to be—and isn't duplicative or conflicting—make sure all your apps talk to each other. Zapier automates the process of moving data from one place to another, so you can focus on the work that matters to move your business forward.

Need a quick summary or still have a few nagging data analysis questions? I'm here for you.

What are the five types of data analysis?

The five types of data analysis are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Each type offers a unique lens for understanding data: text analysis provides insights into text-based content, statistical analysis focuses on numerical trends, diagnostic analysis looks into problem causes, predictive analysis deals with what may happen in the future, and prescriptive analysis gives actionable recommendations.

What is the data analysis process?

The data analysis process involves data decision, collection, cleaning, analysis, interpretation, and visualization. Every stage comes together to transform raw data into meaningful insights. Decision determines what data to collect, collection gathers the relevant information, cleaning ensures accuracy, analysis uncovers patterns, interpretation assigns meaning, and visualization presents the insights.

What is the main purpose of data analysis?

In business, the main purpose of data analysis is to uncover patterns, trends, and anomalies, and then use that information to make decisions, solve problems, and reach your business goals.

Related reading: 

How to get started with data collection and analytics at your business

How to conduct your own market research survey

Automatically find and match related data across apps

How to build an analysis assistant with ChatGPT

What can the ChatGPT data analysis chatbot do?

This article was originally published in October 2022 and has since been updated with contributions from Cecilia Gillen. The most recent update was in September 2023.

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Shea Stevens

Shea is a content writer currently living in Charlotte, North Carolina. After graduating with a degree in Marketing from East Carolina University, she joined the digital marketing industry focusing on content and social media. In her free time, you can find Shea visiting her local farmers market, attending a country music concert, or planning her next adventure.

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Sat / act prep online guides and tips, 5 steps to write a great analytical essay.

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Do you need to write an analytical essay for school? What sets this kind of essay apart from other types, and what must you include when you write your own analytical essay? In this guide, we break down the process of writing an analytical essay by explaining the key factors your essay needs to have, providing you with an outline to help you structure your essay, and analyzing a complete analytical essay example so you can see what a finished essay looks like.

What Is an Analytical Essay?

Before you begin writing an analytical essay, you must know what this type of essay is and what it includes. Analytical essays analyze something, often (but not always) a piece of writing or a film.

An analytical essay is more than just a synopsis of the issue though; in this type of essay you need to go beyond surface-level analysis and look at what the key arguments/points of this issue are and why. If you’re writing an analytical essay about a piece of writing, you’ll look into how the text was written and why the author chose to write it that way. Instead of summarizing, an analytical essay typically takes a narrower focus and looks at areas such as major themes in the work, how the author constructed and supported their argument, how the essay used literary devices to enhance its messages, etc.

While you certainly want people to agree with what you’ve written, unlike with persuasive and argumentative essays, your main purpose when writing an analytical essay isn’t to try to convert readers to your side of the issue. Therefore, you won’t be using strong persuasive language like you would in those essay types. Rather, your goal is to have enough analysis and examples that the strength of your argument is clear to readers.

Besides typical essay components like an introduction and conclusion, a good analytical essay will include:

  • A thesis that states your main argument
  • Analysis that relates back to your thesis and supports it
  • Examples to support your analysis and allow a more in-depth look at the issue

In the rest of this article, we’ll explain how to include each of these in your analytical essay.

How to Structure Your Analytical Essay

Analytical essays are structured similarly to many other essays you’ve written, with an introduction (including a thesis), several body paragraphs, and a conclusion. Below is an outline you can follow when structuring your essay, and in the next section we go into more detail on how to write an analytical essay.

Introduction

Your introduction will begin with some sort of attention-grabbing sentence to get your audience interested, then you’ll give a few sentences setting up the topic so that readers have some context, and you’ll end with your thesis statement. Your introduction will include:

  • Brief background information explaining the issue/text
  • Your thesis

Body Paragraphs

Your analytical essay will typically have three or four body paragraphs, each covering a different point of analysis. Begin each body paragraph with a sentence that sets up the main point you’ll be discussing. Then you’ll give some analysis on that point, backing it up with evidence to support your claim. Continue analyzing and giving evidence for your analysis until you’re out of strong points for the topic. At the end of each body paragraph, you may choose to have a transition sentence that sets up what the next paragraph will be about, but this isn’t required. Body paragraphs will include:

  • Introductory sentence explaining what you’ll cover in the paragraph (sort of like a mini-thesis)
  • Analysis point
  • Evidence (either passages from the text or data/facts) that supports the analysis
  • (Repeat analysis and evidence until you run out of examples)

You won’t be making any new points in your conclusion; at this point you’re just reiterating key points you’ve already made and wrapping things up. Begin by rephrasing your thesis and summarizing the main points you made in the essay. Someone who reads just your conclusion should be able to come away with a basic idea of what your essay was about and how it was structured. After this, you may choose to make some final concluding thoughts, potentially by connecting your essay topic to larger issues to show why it’s important. A conclusion will include:

  • Paraphrase of thesis
  • Summary of key points of analysis
  • Final concluding thought(s)

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5 Steps for Writing an Analytical Essay

Follow these five tips to break down writing an analytical essay into manageable steps. By the end, you’ll have a fully-crafted analytical essay with both in-depth analysis and enough evidence to support your argument. All of these steps use the completed analytical essay in the next section as an example.

#1: Pick a Topic

You may have already had a topic assigned to you, and if that’s the case, you can skip this step. However, if you haven’t, or if the topic you’ve been assigned is broad enough that you still need to narrow it down, then you’ll need to decide on a topic for yourself. Choosing the right topic can mean the difference between an analytical essay that’s easy to research (and gets you a good grade) and one that takes hours just to find a few decent points to analyze

Before you decide on an analytical essay topic, do a bit of research to make sure you have enough examples to support your analysis. If you choose a topic that’s too narrow, you’ll struggle to find enough to write about.

For example, say your teacher assigns you to write an analytical essay about the theme in John Steinbeck’s The Grapes of Wrath of exposing injustices against migrants. For it to be an analytical essay, you can’t just recount the injustices characters in the book faced; that’s only a summary and doesn’t include analysis. You need to choose a topic that allows you to analyze the theme. One of the best ways to explore a theme is to analyze how the author made his/her argument. One example here is that Steinbeck used literary devices in the intercalary chapters (short chapters that didn’t relate to the plot or contain the main characters of the book) to show what life was like for migrants as a whole during the Dust Bowl.

You could write about how Steinbeck used literary devices throughout the whole book, but, in the essay below, I chose to just focus on the intercalary chapters since they gave me enough examples. Having a narrower focus will nearly always result in a tighter and more convincing essay (and can make compiling examples less overwhelming).

#2: Write a Thesis Statement

Your thesis statement is the most important sentence of your essay; a reader should be able to read just your thesis and understand what the entire essay is about and what you’ll be analyzing. When you begin writing, remember that each sentence in your analytical essay should relate back to your thesis

In the analytical essay example below, the thesis is the final sentence of the first paragraph (the traditional spot for it). The thesis is: “In The Grapes of Wrath’s intercalary chapters, John Steinbeck employs a variety of literary devices and stylistic choices to better expose the injustices committed against migrants in the 1930s.” So what will this essay analyze? How Steinbeck used literary devices in the intercalary chapters to show how rough migrants could have it. Crystal clear.

#3: Do Research to Find Your Main Points

This is where you determine the bulk of your analysis--the information that makes your essay an analytical essay. My preferred method is to list every idea that I can think of, then research each of those and use the three or four strongest ones for your essay. Weaker points may be those that don’t relate back to the thesis, that you don’t have much analysis to discuss, or that you can’t find good examples for. A good rule of thumb is to have one body paragraph per main point

This essay has four main points, each of which analyzes a different literary device Steinbeck uses to better illustrate how difficult life was for migrants during the Dust Bowl. The four literary devices and their impact on the book are:

  • Lack of individual names in intercalary chapters to illustrate the scope of the problem
  • Parallels to the Bible to induce sympathy for the migrants
  • Non-showy, often grammatically-incorrect language so the migrants are more realistic and relatable to readers
  • Nature-related metaphors to affect the mood of the writing and reflect the plight of the migrants

#4: Find Excerpts or Evidence to Support Your Analysis

Now that you have your main points, you need to back them up. If you’re writing a paper about a text or film, use passages/clips from it as your main source of evidence. If you’re writing about something else, your evidence can come from a variety of sources, such as surveys, experiments, quotes from knowledgeable sources etc. Any evidence that would work for a regular research paper works here.

In this example, I quoted multiple passages from The Grapes of Wrath  in each paragraph to support my argument. You should be able to back up every claim you make with evidence in order to have a strong essay.

#5: Put It All Together

Now it's time to begin writing your essay, if you haven’t already. Create an introductory paragraph that ends with the thesis, make a body paragraph for each of your main points, including both analysis and evidence to back up your claims, and wrap it all up with a conclusion that recaps your thesis and main points and potentially explains the big picture importance of the topic.

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Analytical Essay Example + Analysis

So that you can see for yourself what a completed analytical essay looks like, here’s an essay I wrote back in my high school days. It’s followed by analysis of how I structured my essay, what its strengths are, and how it could be improved.

One way Steinbeck illustrates the connections all migrant people possessed and the struggles they faced is by refraining from using specific titles and names in his intercalary chapters. While The Grapes of Wrath focuses on the Joad family, the intercalary chapters show that all migrants share the same struggles and triumphs as the Joads. No individual names are used in these chapters; instead the people are referred to as part of a group. Steinbeck writes, “Frantic men pounded on the doors of the doctors; and the doctors were busy.  And sad men left word at country stores for the coroner to send a car,” (555). By using generic terms, Steinbeck shows how the migrants are all linked because they have gone through the same experiences. The grievances committed against one family were committed against thousands of other families; the abuse extends far beyond what the Joads experienced. The Grapes of Wrath frequently refers to the importance of coming together; how, when people connect with others their power and influence multiplies immensely. Throughout the novel, the goal of the migrants, the key to their triumph, has been to unite. While their plans are repeatedly frustrated by the government and police, Steinbeck’s intercalary chapters provide a way for the migrants to relate to one another because they have encountered the same experiences. Hundreds of thousands of migrants fled to the promised land of California, but Steinbeck was aware that numbers alone were impersonal and lacked the passion he desired to spread. Steinbeck created the intercalary chapters to show the massive numbers of people suffering, and he created the Joad family to evoke compassion from readers.  Because readers come to sympathize with the Joads, they become more sensitive to the struggles of migrants in general. However, John Steinbeck frequently made clear that the Joads were not an isolated incident; they were not unique. Their struggles and triumphs were part of something greater. Refraining from specific names in his intercalary chapters allows Steinbeck to show the vastness of the atrocities committed against migrants.

Steinbeck also creates significant parallels to the Bible in his intercalary chapters in order to enhance his writing and characters. By using simple sentences and stylized writing, Steinbeck evokes Biblical passages. The migrants despair, “No work till spring. No work,” (556).  Short, direct sentences help to better convey the desperateness of the migrants’ situation. Throughout his novel, John Steinbeck makes connections to the Bible through his characters and storyline. Jim Casy’s allusions to Christ and the cycle of drought and flooding are clear biblical references.  By choosing to relate The Grapes of Wrath to the Bible, Steinbeck’s characters become greater than themselves. Starving migrants become more than destitute vagrants; they are now the chosen people escaping to the promised land. When a forgotten man dies alone and unnoticed, it becomes a tragedy. Steinbeck writes, “If [the migrants] were shot at, they did not run, but splashed sullenly away; and if they were hit, they sank tiredly in the mud,” (556). Injustices committed against the migrants become greater because they are seen as children of God through Steinbeck’s choice of language. Referencing the Bible strengthens Steinbeck’s novel and purpose: to create understanding for the dispossessed.  It is easy for people to feel disdain for shabby vagabonds, but connecting them to such a fundamental aspect of Christianity induces sympathy from readers who might have otherwise disregarded the migrants as so many other people did.

The simple, uneducated dialogue Steinbeck employs also helps to create a more honest and meaningful representation of the migrants, and it makes the migrants more relatable to readers. Steinbeck chooses to accurately represent the language of the migrants in order to more clearly illustrate their lives and make them seem more like real paper than just characters in a book. The migrants lament, “They ain’t gonna be no kinda work for three months,” (555). There are multiple grammatical errors in that single sentence, but it vividly conveys the despair the migrants felt better than a technically perfect sentence would. The Grapes of Wrath is intended to show the severe difficulties facing the migrants so Steinbeck employs a clear, pragmatic style of writing.  Steinbeck shows the harsh, truthful realities of the migrants’ lives and he would be hypocritical if he chose to give the migrants a more refined voice and not portray them with all their shortcomings. The depiction of the migrants as imperfect through their language also makes them easier to relate to. Steinbeck’s primary audience was the middle class, the less affluent of society. Repeatedly in The Grapes of Wrath , the wealthy make it obvious that they scorn the plight of the migrants. The wealthy, not bad luck or natural disasters, were the prominent cause of the suffering of migrant families such as the Joads. Thus, Steinbeck turns to the less prosperous for support in his novel. When referring to the superior living conditions barnyard animals have, the migrants remark, “Them’s horses-we’re men,” (556).  The perfect simplicity of this quote expresses the absurdness of the migrants’ situation better than any flowery expression could.

In The Grapes of Wrath , John Steinbeck uses metaphors, particularly about nature, in order to illustrate the mood and the overall plight of migrants. Throughout most of the book, the land is described as dusty, barren, and dead. Towards the end, however; floods come and the landscape begins to change. At the end of chapter twenty-nine, Steinbeck describes a hill after the floods saying, “Tiny points of grass came through the earth, and in a few days the hills were pale green with the beginning year,” (556). This description offers a stark contrast from the earlier passages which were filled with despair and destruction. Steinbeck’s tone from the beginning of the chapter changes drastically. Early in the chapter, Steinbeck had used heavy imagery in order to convey the destruction caused by the rain, “The streams and the little rivers edged up to the bank sides and worked at willows and tree roots, bent the willows deep in the current, cut out the roots of cottonwoods and brought down the trees,” (553). However, at the end of the chapter the rain has caused new life to grow in California. The new grass becomes a metaphor representing hope. When the migrants are at a loss over how they will survive the winter, the grass offers reassurance. The story of the migrants in the intercalary chapters parallels that of the Joads. At the end of the novel, the family is breaking apart and has been forced to flee their home. However, both the book and final intercalary chapter end on a hopeful note after so much suffering has occurred. The grass metaphor strengthens Steinbeck’s message because it offers a tangible example of hope. Through his language Steinbeck’s themes become apparent at the end of the novel. Steinbeck affirms that persistence, even when problems appear insurmountable, leads to success. These metaphors help to strengthen Steinbeck’s themes in The Grapes of Wrath because they provide a more memorable way to recall important messages.

John Steinbeck’s language choices help to intensify his writing in his intercalary chapters and allow him to more clearly show how difficult life for migrants could be. Refraining from using specific names and terms allows Steinbeck to show that many thousands of migrants suffered through the same wrongs. Imitating the style of the Bible strengthens Steinbeck’s characters and connects them to the Bible, perhaps the most famous book in history. When Steinbeck writes in the imperfect dialogue of the migrants, he creates a more accurate portrayal and makes the migrants easier to relate to for a less affluent audience. Metaphors, particularly relating to nature, strengthen the themes in The Grapes of Wrath by enhancing the mood Steinbeck wants readers to feel at different points in the book. Overall, the intercalary chapters that Steinbeck includes improve his novel by making it more memorable and reinforcing the themes Steinbeck embraces throughout the novel. Exemplary stylistic devices further persuade readers of John Steinbeck’s personal beliefs. Steinbeck wrote The Grapes of Wrath to bring to light cruelties against migrants, and by using literary devices effectively, he continuously reminds readers of his purpose. Steinbeck’s impressive language choices in his intercalary chapters advance the entire novel and help to create a classic work of literature that people still are able to relate to today. 

This essay sticks pretty closely to the standard analytical essay outline. It starts with an introduction, where I chose to use a quote to start off the essay. (This became my favorite way to start essays in high school because, if I wasn’t sure what to say, I could outsource the work and find a quote that related to what I’d be writing about.) The quote in this essay doesn’t relate to the themes I’m discussing quite as much as it could, but it’s still a slightly different way to start an essay and can intrigue readers. I then give a bit of background on The Grapes of Wrath and its themes before ending the intro paragraph with my thesis: that Steinbeck used literary devices in intercalary chapters to show how rough migrants had it.

Each of my four body paragraphs is formatted in roughly the same way: an intro sentence that explains what I’ll be discussing, analysis of that main point, and at least two quotes from the book as evidence.

My conclusion restates my thesis, summarizes each of four points I discussed in my body paragraphs, and ends the essay by briefly discussing how Steinbeck’s writing helped introduce a world of readers to the injustices migrants experienced during the dust bowl.

What does this analytical essay example do well? For starters, it contains everything that a strong analytical essay should, and it makes that easy to find. The thesis clearly lays out what the essay will be about, the first sentence of each of the body paragraph introduces the topic it’ll cover, and the conclusion neatly recaps all the main points. Within each of the body paragraphs, there’s analysis along with multiple excerpts from the book in order to add legitimacy to my points.

Additionally, the essay does a good job of taking an in-depth look at the issue introduced in the thesis. Four ways Steinbeck used literary devices are discussed, and for each of the examples are given and analysis is provided so readers can understand why Steinbeck included those devices and how they helped shaped how readers viewed migrants and their plight.

Where could this essay be improved? I believe the weakest body paragraph is the third one, the one that discusses how Steinbeck used plain, grammatically incorrect language to both accurately depict the migrants and make them more relatable to readers. The paragraph tries to touch on both of those reasons and ends up being somewhat unfocused as a result. It would have been better for it to focus on just one of those reasons (likely how it made the migrants more relatable) in order to be clearer and more effective. It’s a good example of how adding more ideas to an essay often doesn’t make it better if they don’t work with the rest of what you’re writing. This essay also could explain the excerpts that are included more and how they relate to the points being made. Sometimes they’re just dropped in the essay with the expectation that the readers will make the connection between the example and the analysis. This is perhaps especially true in the second body paragraph, the one that discusses similarities to Biblical passages. Additional analysis of the quotes would have strengthened it.

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Summary: How to Write an Analytical Essay

What is an analytical essay? A critical analytical essay analyzes a topic, often a text or film. The analysis paper uses evidence to support the argument, such as excerpts from the piece of writing. All analytical papers include a thesis, analysis of the topic, and evidence to support that analysis.

When developing an analytical essay outline and writing your essay, follow these five steps:

Reading analytical essay examples can also give you a better sense of how to structure your essay and what to include in it.

What's Next?

Learning about different writing styles in school? There are four main writing styles, and it's important to understand each of them. Learn about them in our guide to writing styles , complete with examples.

Writing a research paper for school but not sure what to write about? Our guide to research paper topics has over 100 topics in ten categories so you can be sure to find the perfect topic for you.

Literary devices can both be used to enhance your writing and communication. Check out this list of 31 literary devices to learn more !

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Importance of Data Analysis Essay

The data analysis process will take place after all the necessary information is obtained and structured appropriately. This will be a basis for the initial stage of the mentioned process – primary data processing. It is important to analyze the results of each study as soon as possible after its completion. So far, the researcher’s memory can suggest those details that, for some reason, are not fixed but are of interest for understanding the essence of the matter. When processing the collected data, it may turn out that they are either insufficient or contradictory and therefore do not give grounds for final conclusions.

In this case, the study must be continued with the required additions. After collecting information from various sources, it is necessary to understand what exactly is needed for the initial analysis of needs in accordance with the task at hand. In most cases, it is advisable to start processing with the compilation of tables (pivot tables) of the data obtained (Simplilearn, 2021). For both manual and computer processing, the initial data is most often entered into the original pivot table. Recently, computer processing has become the predominant form of mathematical and statistical processing.

The second stage is mathematical data processing, which implies a complex preparation. In order to determine the methods of mathematical and statistical processing, first of all, it is important to assess the nature of the distribution for all the parameters used. For parameters that are normally distributed or close to normal, parametric statistics methods can be used, which in many cases are more powerful than nonparametric statistical methods (Ali & Bhaskar, 2016). The advantage of the latter is that they allow testing statistical hypotheses regardless of the shape of the distribution.

One of the most common tasks in data processing is assessing the reliability of differences between two or more series of values. There are a number of ways in mathematical statistics to solve it. The computer version of data processing has become the most widespread today. Many statistical applications have procedures for evaluating the differences between the parameters of the same sample or different samples (Tyagi, 2020). With fully computerized processing of the material, it is not difficult to use the appropriate procedure at the right time and assess the differences of interest.

The following stage may be called the formulation of conclusions. The latter are statements expressing in a concise form the meaningful results of the study. They, in a thesis form, reflect the new findings that were obtained by the author. A common mistake is that the author includes in the conclusions generally accepted in science provisions – no longer needing proof. The responses to each of the objectives listed in the introduction should be reflected in the conclusions in a certain way.

The format for presenting the results after completing the task of analyzing information is of no small importance (Tyagi, 2020). The main content needs to be translated into an easy-to-read format based on their requirements. At the same time, you should provide easy access to additional background data for those who are interested and want to understand the topic more thoroughly. These basic rules apply regardless of the format of the presentation of the information.

In order to successfully solve this problem, special methods of analysis and information processing are required. Classical information technologies make it possible to efficiently store, structure and quickly retrieve information in a user-friendly form. The main strength of SPSS Statistics is the provision of a vast range of instruments that can be utilized in the framework of statistics (Allen et al., 2014). For all the complexity of modern methods of statistical analysis, which use the latest achievements of mathematical science, the SPSS program allows one to focus on the peculiarities of their application in each specific case. This program has capabilities that significantly exceed the scope of functions provided by standard business programs such as Excel.

The SPSS program provides the user with ample opportunities for statistical processing of experimental data, for the formation of databases (SPSS data files), for their modification. SPSS may be considered a complex and flexible statistical analysis tool (Allen et al., 2014). SPSS can take data from virtually any file type and use it to create tabular reports, graphs and distribution maps, descriptive statistics, and sophisticated statistical analysis.

At this point, it seems reasonable to define the sequence of the analysis using the SPSS tools. First, it is essential to draw up a questionnaire with the questions necessary for the researcher. Next, a survey is carried out. To process the received data, you need to draw up a coding table. The coding table establishes the correspondence between individual questions of the questionnaire and the variables used in the computer data processing (Allen et al., 2014). This solves the following tasks; first, a correspondence is established between the individual questions of the questionnaire and the variables. Second, a correspondence is established between the possible values of variables and code numbers.

Next, one needs to enter the data into the data editor according to the defined variables. After that, depending on the task, it is necessary to select the desired function and schedule. Then, you should analyze the subsequent tabular output of the result. All the necessary statistical functions that will be directly used in exploring and analyzing data are located in the Analysis menu. A very important analysis can be done with multiple responses; it is called the dichotomous method. This approach is used in cases when in the questionnaire for answering a question, it is proposed to mark several answer options (Allen et al., 2014).

Comparison of the means of different samples is one of the most commonly used methods of statistical analysis. In this case, the question must always be clarified whether the existing difference in mean values can be explained by statistical fluctuations or not. This method seems appropriate as the study will involve participants from all over the state, and their responses will need to be compared.

It should be stressed that SPSS is the most widely used statistical software. The main advantage of the SPSS software package, as one of the most advanced attainments in the area of automatized data analysis, is the broad coverage of modern statistical approaches. It is successfully combined with a large number of convenient visualization tools for processing results (Allen et al., 2014). The latest version gives notable possibilities not only within the scope of psychology, sociology, and biology but also in the field of medicine, which is crucial for the aims of future research. This greatly expands the applicability of the complex, which will serve as a significant basis for ensuring the validity of the study.

Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anesthesia, 60 (9), 662–669.

Allen, P., Bennet, K., & Heritage, B. (2014). SPSS Statistics version 22: A practical guide . Cengage.

Simplilearn. (2021). What is data analysis: Methods, process and types explained . Web.

Tyagi, N. (2020). Introduction to statistical data analysis . Analytic Steps. Web.

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IvyPanda. (2022, November 30). Importance of Data Analysis. https://ivypanda.com/essays/importance-of-data-analysis/

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Writing a Good Data Analysis Report: 7 Steps

As a data analyst, you feel most comfortable when you’re alone with all the numbers and data. You’re able to analyze them with confidence and reach the results you were asked to find. But, this is not the end of the road for you. You still need to write a data analysis report explaining your findings to the laymen - your clients or coworkers.

That means you need to think about your target audience, that is the people who’ll be reading your report.

They don’t have nearly as much knowledge about data analysis as you do. So, your report needs to be straightforward and informative. The article below will help you learn how to do it. Let’s take a look at some practical tips you can apply to your data analysis report writing and the benefits of doing so.

Writing a Good Data Analysis Report: 7 Steps

source: Pexels  

Data Analysis Report Writing: 7 Steps

The process of writing a data analysis report is far from simple, but you can master it quickly, with the right guidance and examples of similar reports .

This is why we've prepared a step-by-step guide that will cover everything you need to know about this process, as simply as possible. Let’s get to it.

Consider Your Audience

You are writing your report for a certain target audience, and you need to keep them in mind while writing. Depending on their level of expertise, you’ll need to adjust your report and ensure it speaks to them. So, before you go any further, ask yourself:

Who will be reading this report? How well do they understand the subject?

Let’s say you’re explaining the methodology you used to reach your conclusions and find the data in question. If the reader isn’t familiar with these tools and software, you’ll have to simplify it for them and provide additional explanations.

So, you won't be writing the same type of report for a coworker who's been on your team for years or a client who's seeing data analysis for the first time. Based on this determining factor, you'll think about:

the language and vocabulary you’re using

abbreviations and level of technicality

the depth you’ll go into to explain something

the type of visuals you’ll add

Your readers’ expertise dictates the tone of your report and you need to consider it before writing even a single word.

Draft Out the Sections

The next thing you need to do is create a draft of your data analysis report. This is just a skeleton of what your report will be once you finish. But, you need a starting point.

So, think about the sections you'll include and what each section is going to cover. Typically, your report should be divided into the following sections:

Introduction

Body (Data, Methods, Analysis, Results)

For each section, write down several short bullet points regarding the content to cover. Below, we'll discuss each section more elaborately.

Develop The Body

The body of your report is the most important section. You need to organize it into subsections and present all the information your readers will be interested in.

We suggest the following subsections.

Explain what data you used to conduct your analysis. Be specific and explain how you gathered the data, what your sample was, what tools and resources you’ve used, and how you’ve organized your data. This will give the reader a deeper understanding of your data sample and make your report more solid.

Also, explain why you choose the specific data for your sample. For instance, you may say “ The sample only includes data of the customers acquired during 2021, in the peak of the pandemic.”

Next, you need to explain what methods you’ve used to analyze the data. This simply means you need to explain why and how you choose specific methods. You also need to explain why these methods are the best fit for the goals you’ve set and the results you’re trying to reach.

Back up your methodology section with background information on each method or tool used. Explain how these resources are typically used in data analysis.

After you've explained the data and methods you've used, this next section brings those two together. The analysis section shows how you've analyzed the specific data using the specific methods. 

This means you’ll show your calculations, charts, and analyses, step by step. Add descriptions and explain each of the steps. Try making it as simple as possible so that even the most inexperienced of your readers understand every word.

This final section of the body can be considered the most important section of your report. Most of your clients will skim the rest of the report to reach this section. 

Because it’ll answer the questions you’ve all raised. It shares the results that were reached and gives the reader new findings, facts, and evidence. 

So, explain and describe the results using numbers. Then, add a written description of what each of the numbers stands for and what it means for the entire analysis. Summarize your results and finalize the report on a strong note. 

Write the Introduction

Yes, it may seem strange to write the introduction section at the end, but it’s the smartest way to do it. This section briefly explains what the report will cover. That’s why you should write it after you’ve finished writing the Body.

In your introduction, explain:

the question you’ve raised and answered with the analysis

context of the analysis and background information

short outline of the report

Simply put, you’re telling your audience what to expect.

Add a Short Conclusion

Finally, the last section of your paper is a brief conclusion. It only repeats what you described in the Body, but only points out the most important details.

It should be less than a page long and use straightforward language to deliver the most important findings. It should also include a paragraph about the implications and importance of those findings for the client, customer, business, or company that hired you.

Include Data Visualization Elements

You have all the data and numbers in your mind and find it easy to understand what the data is saying. But, to a layman or someone less experienced than yourself, it can be quite a puzzle. All the information that your data analysis has found can create a mess in the head of your reader.

So, you should simplify it by using data visualization elements.

Firstly, let’s define what are the most common and useful data visualization elements you can use in your report:

There are subcategories to each of the elements and you should explore them all to decide what will do the best job for your specific case. For instance, you'll find different types of charts including, pie charts, bar charts, area charts, or spider charts.

For each data visualization element, add a brief description to tell the readers what information it contains. You can also add a title to each element and create a table of contents for visual elements only.

Proofread & Edit Before Submission

All the hard work you’ve invested in writing a good data analysis report might go to waste if you don’t edit and proofread. Proofreading and editing will help you eliminate potential mistakes, but also take another objective look at your report.

First, do the editing part. It includes:

reading the whole report objectively, like you’re seeing it for the first time

leaving an open mind for changes

adding or removing information

rearranging sections

finding better words to say something

You should repeat the editing phase a couple of times until you're completely happy with the result. Once you're certain the content is all tidied up, you can move on to the proofreading stage. It includes:

finding and removing grammar and spelling mistakes

rethinking vocabulary choices

improving clarity 

improving readability

You can use an online proofreading tool to make things faster. If you really want professional help, Grab My Essay is a great choice. Their professional writers can edit and rewrite your entire report, to make sure it’s impeccable before submission.

Whatever you choose to do, proofread yourself or get some help with it, make sure your report is well-organized and completely error-free.

Benefits of Writing Well-Structured Data Analysis Reports

Yes, writing a good data analysis report is a lot of hard work. But, if you understand the benefits of writing it, you’ll be more motivated and willing to invest the time and effort. After knowing how it can help you in different segments of your professional journey, you’ll be more willing to learn how to do it.

Below are the main benefits a data analysis report brings to the table.

Improved Collaboration

When you’re writing a data analysis report, you need to be aware more than one end user is going to use it. Whether it’s your employer, customer, or coworker - you need to make sure they’re all on the same page. And when you write a data analysis report that is easy to understand and learn from, you’re creating a bridge between all these people.

Simply, all of them are given accurate data they can rely on and you’re thus removing the potential misunderstandings that can happen in communication. This improves the overall collaboration level and makes everyone more open and helpful.

Increased Efficiency

People who are reading your data analysis report need the information it contains for some reason. They might use it to do their part of the job, to make decisions, or report further to someone else. Either way, the better your report, the more efficient it'll be. And, if you rely on those people as well, you'll benefit from this increased productivity as well.

Data tells a story about a business, project, or venture. It's able to show how well you've performed, what turned out to be a great move, and what needs to be reimagined. This means that a data analysis report provides valuable insight and measurable KPIs (key performance indicators) that you’re able to use to grow and develop. 

Clear Communication

Information is key regardless of the industry you're in or the type of business you're doing. Data analysis finds that information and proves its accuracy and importance. But, if those findings and the information itself aren't communicated clearly, it's like you haven't even found them.

This is why a data analysis report is crucial. It will present the information less technically and bring it closer to the readers.

Final Thoughts

As you can see, it takes some skill and a bit more practice to write a good data analysis report. But, all the effort you invest in writing it will be worth it once the results kick in. You’ll improve the communication between you and your clients, employers, or coworkers. People will be able to understand, rely on, and use the analysis you’ve conducted.

So, don’t be afraid and start writing your first data analysis report. Just follow the 7 steps we’ve listed and use a tool such as ProWebScraper to help you with website data analysis. You’ll be surprised when you see the result of your hard work.

Jessica Fender

Jessica Fender is a business analyst and a blogger. She writes about business and data analysis, networking in this sector, and acquiring new skills. Her goal is to provide fresh and accurate information that readers can apply instantly.

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Beyond Numbers: the Significance of Qualitative Data Analysis

This essay about the evolving landscape of research emphasizes the historical emphasis on quantitative data and the emerging recognition of the indispensable role played by qualitative data analysis. It explores the meticulous nature of qualitative analysis in navigating non-numeric information, revealing nuanced insights into the “why” and “how” behind human experiences and behaviors. Through a hypothetical study on community development, the essay illustrates how qualitative data unveils the human stories behind quantitative improvements, highlighting the struggles, cultural nuances, and unintended consequences. The essay underscores the flexibility of qualitative analysis, its contribution to theory building, and its ability to enhance the credibility of research findings by incorporating diverse perspectives. While acknowledging challenges, proponents argue that rigorous methodologies can mitigate concerns, ultimately advocating for the synergy of numbers and narratives to unlock a profound understanding of our world.

How it works

In the ever-evolving landscape of research and analysis, the spotlight has historically shone brightly on quantitative data, offering a seemingly unassailable foundation for drawing conclusions. However, a paradigm shift is underway, acknowledging the irreplaceable role played by qualitative data analysis. Beyond the stark numerical arrays inhabiting spreadsheets and statistical models, a treasure trove of nuanced insights awaits discovery through a more interpretive and thorough exploration.

Qualitative data analysis, fundamentally, is a meticulous and rigorous expedition into non-numeric information. It entails navigating through a rich tapestry of narratives, perspectives, and contexts that resist encapsulation within numerical values alone.

While quantitative data may lay out the “what” and “how much,” qualitative data plunges into the realms of “why” and “how,” breathing vitality into statistics and providing a profound understanding of the intricacies shaping human experiences and behaviors.

The strength of qualitative data analysis lies in its capacity to grasp the complexity and subtlety inherent in social phenomena. Numbers, however comprehensive, often stumble when unraveling the intricacies of human behavior, cultural dynamics, and social interactions. Qualitative data steps in as the missing piece, enabling researchers to explore the subjective meanings individuals attach to their experiences and the social contexts shaping their perspectives.

Imagine a study investigating the impact of a community development program on a marginalized population. While quantitative data might unveil improvements in measurable outcomes, qualitative data analysis would unveil the human stories behind these numbers – the struggles to access resources, the cultural nuances influencing perceptions, and the unintended consequences not apparent in quantitative metrics alone.

Furthermore, qualitative data analysis provides researchers with the flexibility to adapt and refine their approach as the study unfolds. Unlike the rigid structures of quantitative research, qualitative analysis allows for iterative processes. Researchers can adjust their focus, explore emerging themes, and refine inquiries based on evolving insights into the subject matter. This adaptability is particularly valuable in exploring complex and multifaceted issues where predefined categories may fall short.

In addition to illuminating the human side of data, qualitative analysis significantly contributes to theory building and hypothesis generation. Immersed in the narratives and experiences of participants, researchers can identify patterns, relationships, and underlying mechanisms that inform the development of new theoretical frameworks. This qualitative foundation enriches quantitative research, offering a more comprehensive understanding of phenomena.

Moreover, qualitative data analysis enhances the credibility and trustworthiness of research findings. By incorporating diverse perspectives and contextual nuances, it adds a layer of validity beyond statistical significance. Policymakers, practitioners, and stakeholders find qualitative insights compelling, resonating with the lived experiences of individuals and communities, making research findings more relevant and applicable.

While acknowledging the challenges and criticisms associated with qualitative data analysis—such as inherent subjectivity and potential bias—proponents argue that these concerns can be mitigated through rigorous research design, transparent methodology, and the integration of multiple data sources.

In conclusion, the importance of qualitative data analysis stretches beyond conventional research methodologies. It injects depth and nuance into our comprehension of complex phenomena, unveiling narratives behind the numbers. As the research landscape evolves, recognizing the complementary roles of qualitative and quantitative approaches becomes essential for generating comprehensive and impactful insights. The synergy of numbers and narratives holds the key to unlocking a profound understanding of our world.

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  • Textual Analysis | Guide, 3 Approaches & Examples

Textual Analysis | Guide, 3 Approaches & Examples

Published on November 8, 2019 by Jack Caulfield . Revised on June 22, 2023.

Textual analysis is a broad term for various research methods used to describe, interpret and understand texts. All kinds of information can be gleaned from a text – from its literal meaning to the subtext, symbolism, assumptions, and values it reveals.

The methods used to conduct textual analysis depend on the field and the aims of the research. It often aims to connect the text to a broader social, political, cultural, or artistic context. Relatedly, it’s good to be careful of confirmation bias when conducting these sorts of analyses, grounding your observations in clear and plausible ways.

Table of contents

What is a text, textual analysis in cultural and media studies, textual analysis in the social sciences, textual analysis in literary studies, other interesting articles.

The term “text” is broader than it seems. A text can be a piece of writing, such as a book, an email, or a transcribed conversation. But in this context, a text can also be any object whose meaning and significance you want to interpret in depth: a film, an image, an artifact, even a place.

The methods you use to analyze a text will vary according to the type of object and the purpose of your analysis:

  • Analysis of a short story might focus on the imagery, narrative perspective and structure of the text.
  • To analyze a film, not only the dialogue but also the cinematography and use of sound could be relevant to the analysis.
  • A building might be analyzed in terms of its architectural features and how it is navigated by visitors.
  • You could analyze the rules of a game and what kind of behaviour they are designed to encourage in players.

While textual analysis is most commonly applied to written language, bear in mind how broad the term “text” is and how varied the methods involved can be.

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In the fields of cultural studies and media studies, textual analysis is a key component of research. Researchers in these fields take media and cultural objects – for example, music videos, social media content, billboard advertising – and treat them as texts to be analyzed.

Usually working within a particular theoretical framework (for example, using postcolonial theory, media theory, or semiotics), researchers seek to connect elements of their texts with issues in contemporary politics and culture. They might analyze many different aspects of the text:

  • Word choice
  • Design elements
  • Location of the text
  • Target audience
  • Relationship with other texts

Textual analysis in this context is usually creative and qualitative in its approach. Researchers seek to illuminate something about the underlying politics or social context of the cultural object they’re investigating.

In the social sciences, textual analysis is often applied to texts such as interview transcripts and surveys , as well as to various types of media. Social scientists use textual data to draw empirical conclusions about social relations.

Textual analysis in the social sciences sometimes takes a more quantitative approach , where the features of texts are measured numerically. For example, a researcher might investigate how often certain words are repeated in social media posts, or which colors appear most prominently in advertisements for products targeted at different demographics.

Some common methods of analyzing texts in the social sciences include content analysis , thematic analysis , and discourse analysis .

Textual analysis is the most important method in literary studies. Almost all work in this field involves in-depth analysis of texts – in this context, usually novels, poems, stories or plays.

Because it deals with literary writing, this type of textual analysis places greater emphasis on the deliberately constructed elements of a text: for example, rhyme and meter in a poem, or narrative perspective in a novel. Researchers aim to understand and explain how these elements contribute to the text’s meaning.

However, literary analysis doesn’t just involve discovering the author’s intended meaning. It often also explores potentially unintended connections between different texts, asks what a text reveals about the context in which it was written, or seeks to analyze a classic text in a new and unexpected way.

Some well-known examples of literary analysis show the variety of approaches that can be taken:

  • Eve Kosofky Sedgwick’s book Between Men analyzes Victorian literature in light of more contemporary perspectives on gender and sexuality.
  • Roland Barthes’ S/Z provides an in-depth structural analysis of a short story by Balzac.
  • Harold Bloom’s The Anxiety of Influence applies his own “influence theory” to an analysis of various classic poets.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles
  • Cluster sampling
  • Stratified sampling
  • Thematic analysis
  • Cohort study
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  • Availability heuristic
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